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Uber's data visualisation. Source:

At 8:30pm, there is a downfall of rain and suddenly there is a significant spike in users. Instead of 900 users opening the app like an hour ago, there are now 2000 users in the 30km radius who have opened the app in between the hours of 8:30pm and 8:35pm. The Uber team knows that the conversion rate from app opens to ride booked also increases during significant rainfall to 15% which is up from 10% from pre-rainfall.


Compared to an hour ago, where 90 users were looking to book a ride from the 100 drivers, now 300 users are expected to book a ride from the 100 drivers.


Uber’s Pricing algorithms will automatically apply a 2.4% increase surge price. The Uber team knows in advance that this price surge will reduce the conversion rate from 15% to 5% and that it will also encourage another 50 drivers onto the road in the area to help meet demand.


For the next 5-10 minutes the surge pricing is in effect, reducing demand and increasing supply. Once it has brought about sufficient balance, pricing is reduced and some of those that were deterred from conversion at 8:30pm typically convert.


The most important concept for the Ecommerce Business Leader in this example is the demand indicator. Uber has a very simple, mono-indicator because they’re ultimately only selling a single product; drivers. They use app opens as the primary indicator of market size for their single product. Whereas in the Ecommerce model, there are implementations that are looking for product interest indicators.


Some raise objections that Uber’s model is too simple, and that Dynamic Pricing cannot be applied to larger Ecommerce retailers. The reality is that every Ecommerce Store is a market, and all are subject to the same economic laws of supply and demand. The main point of difference is that the ecommerce store is more complicated and data intensive. Yet this very complication is why artificial intelligence is yielding such significant increases in operating margins for these stores, because it can process the huge amounts of data generated to understand the pricing power of the company and deliver actionable insights.

So just like Uber, Ecommerce Retailers are now starting to utilise artificial intelligence to find the market imbalances and to increase revenue. If you’d like to learn more about A.I price optimisation and how it’s being used by others, head over to our website.

Dynamic Pricing for Ecommerce

Mar 2020, Sydney

Eamonn Barrett

Back in 2012, one of the US Uber teams noticed a significant problem in their operations. Late on Friday nights, just after 1am, the app was seeing a huge spike in ‘failed requests.” These failed requests were where users were requesting a ride and there was no driver available in a suitable radius to meet their request. The cause behind these failed requests was that drivers were finishing for the night to go home and sleep- and this was only one hour before there was a significant spike in party-goers trying to return home. The result? Many disgruntled and inebriated users.

In economic theory, this is what is considered a supply-demand imbalance: where supply cannot meet demand expectations of the market. For the majority of companies this is a significant problem for both reputation and loss of sales where customers quickly turn to other suppliers and the company becomes associated with being out of stock. It is only in strong product monopolies, where the customer doesn’t have appealing alternatives, that this supply-demand imbalance can be utilised to the advantage of the company. Such an example of this was the earlier iPhones, where Apple were deliberately using the shortage of the product to build hype around the popularity of the product.


But in Uber’s case, these party-goers could easily revert to the traditional hailing of a cab. 


So the Uber team came up with a solution. What if they offered the drivers a higher price to stay out longer? If they used a multiplier on the standard rate, would this encourage more drivers to stay out or even return out to the streets? Would the money a driver earned increase? 


In just over two weeks, after a series of trials, Uber had an answer. By offering an increased surge rate to drivers, they were able to increase the number of drivers on the road by 80%, effectively reducing the failed rides by 65%. This was a resounding success. Ultimately,  drivers were motivated by the increased pay and that more people got home.


This simple demand model is not as sophisticated as those being applied to modern Ecommerce Businesses, but the basic concepts apply to any marketplace, including Ecommerce.


When it comes to Artificial Intelligence, demand is probably the most interesting concept in the Ecommerce pricing strategy. To highlight this concept, it’s worth sticking with Uber for a little longer with the following hypothetical;


It is 7:30pm on a Friday night in the Western Suburbs of Sydney. There are a total of 100 Uber Drivers in a 30km radius. When Uber’s pricing is normal and not surging, there is a 10% likelihood that a user who opens the app will book a ride in the next 5 minutes. At the current time, there are 900 people in the 30km radius who have opened the app. So, using probability rules, 90 of the 900 users will request a ride. This means that 90% of the drivers, who in Uber’s market model can be considered the inventory, will be booked.

How do we understand demand in the Ecommerce framework?

Using demand to understand pricing power

“The single most important decision in evaluating a business is pricing power.” 

                                                                                                Warren Buffett, CEO Berkshire Hathaway

Pricing Power is the relationship between your business, your competitors, your suppliers and your customers. It is ultimately the power your business holds to increase the price above the margin cost of the product you’re selling.


A.I price optimisation is proving to be the quickest and most intelligent way to understand and capitalise on your pricing power. Solutions such as Remi AI’s Price Optimisation Platform allow you to quickly capitalise on previously lost revenue across your entire product range.


It is continually trying to understand the company’s pricing power and the significance of each competitor’s pricing across each SKU. This results in an outperforming of existing manual rules based systems. For one of our clients, we have seen A.I price optimisation outperform the rules-based strategy of experienced sellers by more than 20%.

Summing up

Beyond the usual demand drivers that can be ingested by forecasting algorithms such as Sales History, Prices, and Promotions, there are several other key data points available only to ecommerce businesses that can provide invaluable demand signals. On-site search and product viewings are two of the strongest indicators we use to model demand for individual products. The beauty of this data is that it can be live, that is, pricing and forecasting algorithms can be plugged into Shopify, Magento, Wix (or wherever else you decide to run your ecommerce store) listening for these short term demand signals every second of the day. This is not something that is easily achieved by a human team, and is a key driver of success in from A.I in the pricing domain in recent times.

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